On Expressivity and Trainability of Quadratic Networks

Inspired by the diversity of biological neurons, quadratic artificial neurons can play an important role in deep learning models. The type of quadratic neurons of our interest replaces the inner-product operation in the conventional neuron with a quadratic function. Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed. Theoretically, the superior expressivity of a quadratic network over either a conventional network or a conventional network via quadratic activation is not fully elucidated, which makes the use of quadratic networks not well grounded. Practically, although a quadratic network can be trained via generic backpropagation, it can be subject to a higher risk of collapse than the conventional counterpart. To address these issues, we first apply the spline theory and a measure from algebraic geometry to give two theorems that demonstrate better model expressivity of a quadratic network than the conventional counterpart with or without quadratic activation. Then, we propose an effective and efficient training strategy referred to as ReLinear to stabilize the training process of a quadratic network, thereby unleashing the full potential in its associated machine learning tasks. Comprehensive experiments on popular datasets are performed to support our findings and evaluate the performance of quadratic deep learning

Motion Correction via Locally Linear Embedding for Helical Photon-counting CT

X-ray photon-counting detector (PCD) offers low noise, high resolution, and spectral characterization, representing a next generation of CT and enabling new biomedical applications. It is well known that involuntary patient motion may induce image artifacts with conventional CT scanning, and this problem becomes more serious with PCD due to its high detector pitch and extended scan time. Furthermore, PCD often comes with a substantial number of bad pixels, making analytic image reconstruction challenging and ruling out state-of-the-art motion correction methods that are based on analytical reconstruction. In this paper, we extend our previous locally linear embedding (LLE) cone-beam motion correction method to the helical scanning geometry, which is especially desirable given the high cost of large-area PCD. In addition to our adaption of LLE-based parametric searching to helical cone-beam photon-counting CT geometry, we introduce an unreliable-volume mask to improve the motion estimation accuracy and perform incremental updating on gradually refined sampling grids for optimization of both accuracy and efficiency. Our numerical results demonstrate that our method reduces the estimation errors near the two longitudinal ends of the reconstructed volume and overall image quality. The experimental results on clinical photon-counting scans of the patient extremities show significant resolution improvement after motion correction using our method, which reveals subtle fine structures previously hidden under motion blurring and artifacts

The traditional Chinese medicine and non-small cell lung cancer: from a gut microbiome perspective

Non-small cell lung cancer (NSCLC) is one of the most serious diseases affecting human health today, and current research is focusing on gut flora. There is a correlation between intestinal flora imbalance and lung cancer, but the specific mechanism is not clear. Based on the “lung and large intestine being interior-exteriorly related” and the “lung-intestinal axis” theory. Here, based on the theoretical comparisons of Chinese and western medicine, we summarized the regulation of intestinal flora in NSCLC by active ingredients of traditional Chinese medicine and Chinese herbal compounds and their intervention effects, which is conducive to providing new strategies and ideas for clinical prevention and treatment of NSCLC

ATF5 promotes malignant T cell survival through the PI3K/AKT/mTOR pathway in cutaneous T cell lymphoma

BackgroundsCutaneous T cell lymphoma (CTCL) is a non-Hodgkin lymphoma characterized by skin infiltration of malignant T cells. The biological overlap between malignant T cells and their normal counterparts has brought obstacles in identifying tumor-specific features and mechanisms, limiting current knowledge of CTCL pathogenesis. Transcriptional dysregulation leading to abnormal gene expression profiles contributes to the initiation, progression and drug resistance of cancer. Therefore, we aimed to identify tumor-specific transcription factor underlying CTCL pathology.MethodsWe analyzed and validated the differentially expressed genes (DEGs) in malignant T cells based on single-cell sequencing data. Clinical relevance was evaluated based on progression-free survival and time to next treatment. To determine the functional importance, lentivirus-mediated gene knockdown was conducted in two CTCL cell lines Myla and H9. Cell survival was assessed by examining cell viability, colony-forming ability, in-vivo tumor growth in xenograft models, apoptosis rate and cell-cycle distribution. RNA sequencing was employed to investigate the underlying mechanisms.ResultsActivating transcription factor 5 (ATF5) was overexpressed in malignant T cells and positively correlated with poor treatment responses in CTCL patients. Mechanistically, ATF5 promoted the survival of malignant T cells partially through the PI3K/AKT/mTOR pathway, and imparted resistance to endoplasmic reticulum (ER) stress-induced apoptosis.ConclusionsThese findings revealed the tumor-specific overexpression of the transcription factor ATF5 with its underlying mechanisms in promoting tumor survival in CTCL, providing new insight into the understanding of CTCL’s pathology

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance